Ms. Ariana Allman Profile

Ariana Allman

at Canon Stroke and Vascular Research Ctr

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Author

Publications (5)

Proceedings Article | 16 March 2020 Paper

Effect of truncated singular value decomposition on digital subtraction angiography derived angiographic parametric imaging maps

Ryan Rava, Ariana Allman, Stephen Rudin, Ciprian Ionita

Proceedings Volume 11312, 1131235 (2020) https://doi.org/10.1117/12.2545994

KEYWORDS: Arteries, Angiography, Statistical modeling, Blood, Aneurysms, Hemodynamics, Ischemic stroke, Artificial intelligence, Software development, Tissues

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Proceedings Article | 2 March 2020 Presentation + Paper

Use of 3D printed intracranial aneurysm phantoms to test the effect of flow diverters geometry on hemodynamics

Ariana Allman, Mohammad Mahdi Shiraz Bhurwani, Jillian Senko, Ryan Rava, Alexander Podgorsak, Stephan Rudin, Ciprian Ionita

Proceedings Volume 11318, 1131803 (2020) https://doi.org/10.1117/12.2549575

KEYWORDS: Resistance, Aneurysms, Arteries, Hemodynamics, 3D modeling, 3D printing

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Proceedings Article | 2 March 2020 Presentation + Paper

Challenges and limitations of patient specific mitral valve 3D-printing

Jillian Senko, Alexander Podgorsak, Ariana Allman, Ryan Rava, Mohammad Mahdi Shiraz Bhurwani, Vijay Iyer, Stephen Rudin, Ciprian Ionita

Proceedings Volume 11318, 1131805 (2020) https://doi.org/10.1117/12.2549140

KEYWORDS: 3D modeling, Image segmentation, 3D printing, Computed tomography, 3D image processing, Tissues, Manufacturing, Blood, Electrocardiography, Heart

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Proceedings Article | 15 March 2019 Presentation + Paper

Controlled compliancy of 3D printed vascular patient specific phantoms

Ariana Allman, Lauren Shepard, Alexander Podgorsak, Ryan Rava, Ciprian Ionita

Proceedings Volume 10954, 109540C (2019) https://doi.org/10.1117/12.2512528

KEYWORDS: Arteries, Liquids, 3D printing, Angiography, 3D modeling, Blood, Image segmentation, Blood circulation, Control systems, X-rays

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Purpose: 3D printed (3DP) patient specific vascular phantoms provide the ability to improve device testing and to aid in the course of treatment of vascular disease, while reducing the need for in-vivo experiments. In addition to accurate vascular geometric reproducibility, such phantoms could allow simulation of certain vascular mechanical properties. We investigated various 3DP designs to allow simulation of physiological transmural pressure on phantom vasculature. Materials and Methods: A transparent compliance chamber was created using an Eden260V printer (Stratasys) with VeroClear and acrylic to accommodate 3DP patient specific vascular phantoms. The patient vascular geometries were acquired from a CT angiogram (Aquilion ONE, Canon Medical) and segmented using Vitrea workstation (Vital Images). The segmented geometry was manipulated in Autodesk Meshmixer and 3D printed using Agilus. The phantom was integrated in the compliance chamber and connected to a pump which simulated physiologic pulsatile flow waveforms. Compliance of the vessels was varied by filling the chamber with various levels of liquid and air. This setup allowed controlled expansion of the 3DP arteries, as a function of the liquid level while a programmable pump simulated the blood flow through the vascular network. The pressure within the vessels was measured for various compliancy settings while physiological flow rates were simulated through the arteries. Results: A neurovascular phantom was placed in the chamber and amount of artery expansion diameter was controlled by changing the liquid level in the compliance chamber. Artery patency and contrast flow were demonstrated using x-ray angiography. The pressures in the left and the right internal carotid artery increased from 98mmHg to 104mmHg and from 96mmHg to 102mmHg, respectively, while maintaining the same flow rates. Conclusions: 3D printed patient specific neurovascular phantoms can be manipulated through using of a compliance chamber in order to establish physiologically relevant hemodynamic conditions.

Proceedings Article | 1 March 2019 Paper

Feasibility of locating infarct core with 2D angiographic parametric imaging (API) using computed tomography perfusion data

Ryan Rava, Ariana Allman, Mohammad Mahdi Shiraz Bhurwani, Kenneth Snyder, Elad Levy, Adnan Siddiqui, Jason Davies, Ciprian Ionita

Proceedings Volume 10948, 109485T (2019) https://doi.org/10.1117/12.2511980

KEYWORDS: Tissues, Brain, Targeting Task Performance metric, Angiography, Computed tomography, Ischemic stroke, LabVIEW, Brain mapping, Software development, Blood circulation

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